Artificial Intelligence in Games I 1000-2M13SIG
1. Solving puzzles – Monte-Carlo based methods: NMCS, NRPA.
2. Solving games with small state space – back propagation method.
3. Game tree search – minmax, alpha-beta and variants.
4. Alfa-beta parallelization.
5. Hashing and transposition table.
6. Building evaluation function – patters and regression methods.
7. Monte-Carlo Tree-Search – game tree search by random simulations.
8. Heuristics in MCTS – RAVE, progressive bias, progressive widening.
9. Parallelizing MCTS.
10. Proof number search and its depth-first version (DFPN) – solving games algorithm.
11. Parallelizing DFPN.
12. Conspiracy number search – PN-search variant with evaluation function.
13. Games with chance – expectimax, adaptation of MCTS i CN-search.
14. Meta-heuristics for parameter optimization – CLOP.
Type of course
Prerequisites (description)
Assessment criteria
Mark is based on few programming tasks plus one large individual project. Possibility of raising the mark by presenting personally learned material.
Bibliography
Course material is mainly based on publications. The list of publications is refreshed on course homepage: http://www.mimuw.edu.pl/~pan/sig/. Most of these papers can be found on:
http://chessprogramming.wikispaces.com/
http://computer-go.org/mailman/listinfo/computer-go
http://pl.wikipedia.org/wiki/Monte-Carlo_Tree_Search
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: